Using graphic illustrations to uncover how a community of practice can influence the delivery of compassionate healthcare
Bibliographic record
Abstract
Our modern-day frenetic healthcare culture has progressed to a state where healthcare professionals tend to detach themselves from the emotions of their patients/clients, rather than embed compassion into their daily practice. The AMS Phoenix Project: A Call to Caring was implemented with the goal to instil and sustain empathy and compassion in environments where clinicians learn and work. The purpose of this study is to report on how an interprofessional community of practice (CoP) of healthcare educators can contribute to a cultural shift in promoting and delivering compassion in healthcare through health professionals education. Using an imaginative creative autoethnography that adopts a narrative design through graphic illustrations, data were collected from 25 members of the Phoenix@Queen’s CoP during a 1-day retreat. Data collection included a graphic recorder who visually depicted all retreat dialogue, field notes that highlighted emergent themes and artefacts produced during the day. Audio recordings of the discussions were used as secondary sources of data. Using thematic analysis, three themes emerged: the call to caring is a long and winding road with many barriers and rewards; CoP members experienced personal growth in and through the community; and the Phoenix@Queen’s CoP matters in terms of professional relationships, leadership and moving forward a shared agenda about practising compassionate healthcare. This study describes the development of a CoP that moves away from traditional committees and discussions to an experiential creation of connections and shared meaning by its members. By using autoethnography, and by demonstrating how graphic illustration can be an innovative and creative method for recording and interpreting group discussions, we have demonstrated the accelerated development of an authentic CoP. With a richer and more authentic community, the shared goals of healthcare professional educators are more likely to be achieved.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.014 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".